{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JDJZRO33JPLCPSCY6PRGCPMD4V","short_pith_number":"pith:JDJZRO33","schema_version":"1.0","canonical_sha256":"48d398bb7b4bd627c858f3e2613d83e57fd14edef25ba930d78ac50eeaca8f39","source":{"kind":"arxiv","id":"1711.09598","version":2},"attestation_state":"computed","paper":{"title":"Diffusion Maps Kalman Filter for a Class of Systems with Gradient Flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SP","authors_text":"Jean-Jacques Slotine, Ronen Talmon, Tal Shnitzer","submitted_at":"2017-11-27T09:42:56Z","abstract_excerpt":"In this paper, we propose a non-parametric method for state estimation of high-dimensional nonlinear stochastic dynamical systems, which evolve according to gradient flows with isotropic diffusion. We combine diffusion maps, a manifold learning technique, with a linear Kalman filter and with concepts from Koopman operator theory. More concretely, using diffusion maps, we construct data-driven virtual state coordinates, which linearize the system model. Based on these coordinates, we devise a data-driven framework for state estimation using the Kalman filter. We demonstrate the strengths of our"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1711.09598","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2017-11-27T09:42:56Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"3a603fe82a21226947ea96e2e82a38c77a9895b51d9474a43f777f16c154b300","abstract_canon_sha256":"ce6b62111ffd1920ea85f858844860cc67bcd0116241afc432a8d5dd1dcd8552"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:55.902653Z","signature_b64":"R+Fz+eEDnQaHH271ynzXNsi+Y5iIJa+0XCa8dQPoe7QjTBtHbqM/8BxT1Pi6P7u/b1dpzWfSrih9JT1ZKaQPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48d398bb7b4bd627c858f3e2613d83e57fd14edef25ba930d78ac50eeaca8f39","last_reissued_at":"2026-05-17T23:52:55.901890Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:55.901890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion Maps Kalman Filter for a Class of Systems with Gradient Flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SP","authors_text":"Jean-Jacques Slotine, Ronen Talmon, Tal Shnitzer","submitted_at":"2017-11-27T09:42:56Z","abstract_excerpt":"In this paper, we propose a non-parametric method for state estimation of high-dimensional nonlinear stochastic dynamical systems, which evolve according to gradient flows with isotropic diffusion. We combine diffusion maps, a manifold learning technique, with a linear Kalman filter and with concepts from Koopman operator theory. More concretely, using diffusion maps, we construct data-driven virtual state coordinates, which linearize the system model. Based on these coordinates, we devise a data-driven framework for state estimation using the Kalman filter. We demonstrate the strengths of our"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.09598","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.09598","created_at":"2026-05-17T23:52:55.902011+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.09598v2","created_at":"2026-05-17T23:52:55.902011+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.09598","created_at":"2026-05-17T23:52:55.902011+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDJZRO33JPLC","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDJZRO33JPLCPSCY","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDJZRO33","created_at":"2026-05-18T12:31:21.493067+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V","json":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V.json","graph_json":"https://pith.science/api/pith-number/JDJZRO33JPLCPSCY6PRGCPMD4V/graph.json","events_json":"https://pith.science/api/pith-number/JDJZRO33JPLCPSCY6PRGCPMD4V/events.json","paper":"https://pith.science/paper/JDJZRO33"},"agent_actions":{"view_html":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V","download_json":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V.json","view_paper":"https://pith.science/paper/JDJZRO33","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.09598&json=true","fetch_graph":"https://pith.science/api/pith-number/JDJZRO33JPLCPSCY6PRGCPMD4V/graph.json","fetch_events":"https://pith.science/api/pith-number/JDJZRO33JPLCPSCY6PRGCPMD4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V/action/storage_attestation","attest_author":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V/action/author_attestation","sign_citation":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V/action/citation_signature","submit_replication":"https://pith.science/pith/JDJZRO33JPLCPSCY6PRGCPMD4V/action/replication_record"}},"created_at":"2026-05-17T23:52:55.902011+00:00","updated_at":"2026-05-17T23:52:55.902011+00:00"}